Poincare Maps projection on Kinases starting from scratch

import os
import numpy as np
import pandas as pd
#import plotly
#import plotly.express as px
from pplots_new import read_embeddings, plot_embedding, plot_embedding_interactive, rotate, get_colors
import warnings
warnings.filterwarnings('ignore')

%matplotlib inline

Data preparation

The user must provide a path to the input file in .mfasta format and path to the output directory for intermediate file storage:

path_to_PM = "/home/lavande/galochkina/SCIENCE/POINCARE/PoincareMSA"
mfasta = path_to_PM+"/examples/kinases/kinases.mfasta" # full path to the input MSA in mfasta format
path_out = path_to_PM+"/examples/kinases_test" # a directory to write resutling files
out_name = "kinases" # name given to the output files
path_to_figures = "/home/lavande/galochkina/SCIENCE/POINCARE/PoincareMSA/figures"

All scripts necessary for data preparation are located in scirpts/data_preparation:

path_prep_scripts = path_to_PM+"/scripts/prepare_data"

Data preparation consists in .mfasta cleaning according to a gap threshold and translation of each sequence to the PSSM profile:

gapth = "0.9" # threshold for filtering gapped positions
prep_parameters = path_prep_scripts+" "+mfasta+" "+path_out+" "+out_name+" "+gapth # parameters for data preparation
# print(prep_parameters)
# print(path_prep_scripts+"/create_projection.sh " + prep_parameters)

os.system(path_prep_scripts+"/create_projection.sh " + prep_parameters)
print("Output files ready for projection are written to: "+path_out+"/fasta"+gapth)

Output files ready for projection are written to: /home/lavande/galochkina/SCIENCE/POINCARE/PoincareMSA/examples/kinases_test/fasta0.9

Data projection using Poincaré disk

You can change the parameters of the projection here:

knn = "5"
gamma = "2.00"
sigma = "1.00"
batchs = "4"
cospca = "0"
epochs = "1000"
seed = "0"

Then, the following command creates a projection of encoded sequences to a Poincaré disk:

path_to_build_PM = path_to_PM+"/scripts/build_poincare_map"
pm_command = "python "+path_to_build_PM+"/main.py --input_path "+path_out+"/fasta"+gapth+" --output_path "+path_out+"/projections/ --gamma "+gamma+" --pca "+ cospca+" --epochs "+epochs+" --seed "+seed
print(pm_command)
os.system(pm_command)

python /home/lavande/galochkina/SCIENCE/POINCARE/PoincareMSA/scripts/build_poincare_map/main.py --input_path /home/lavande/galochkina/SCIENCE/POINCARE/PoincareMSA/examples/kinases_test/fasta0.9 --output_path /home/lavande/galochkina/SCIENCE/POINCARE/PoincareMSA/examples/kinases_test/projections/ --gamma 2.00 --pca 0 --epochs 1000 --seed 0

0

Parameters by default are provided in comments. The output files are then written to the following file:

path_embedding = path_out+"/projections/PM"+knn+"sigma="+sigma+"gamma="+gamma+"cosinepca="+cospca+"_seed"+seed+".csv"
print(path_embedding)

/home/lavande/galochkina/SCIENCE/POINCARE/PoincareMSA/examples/kinases_test/projections/PM5sigma=1.00gamma=2.00cosinepca=0_seed0.csv

Projection visualization

One can visualieze the resulting projection using any convenient coloring. To do so, the user shoud provide a .csv file with each line corresponding to a protein:

path_annotation = path_to_PM+"/visualization/data/kinase_group_new.csv" # path to annotation file
kinase_df = pd.read_csv(path_annotation, index_col = 0)
kinase_df

	1_Group	2_Gene	3_HGNC	4_Uni_entry	5_Uni_acc	6_Domain_begin	7_Domain_end	8_Domain_length	9_Largest_insert_length	10_PDB_validation	11_Conformational_state	12_Dihedral_state	13_Group_in_Uni	14_Group_in_Manning	15_Synonymn	evo_distance	decile_domain
proteins_id
1	AGC	AKT1	HGNC:391	AKT1_HUMAN	P31749	150	408	259	9	6NPZB	DFGin	BLAminus	AGC	AGC	PKB,RAC	1.208266	2
2	AGC	AKT2	HGNC:392	AKT2_HUMAN	P31751	152	409	258	9	3E8DB	DFGin	BLAminus	AGC	AGC	NaN	1.208312	2
3	AGC	AKT3	HGNC:393	AKT3_HUMAN	Q9Y243	148	405	258	9	NaN	NaN	NaN	AGC	AGC	PKBG	1.183463	2
4	AGC	CDC42BPA	HGNC:1737	MRCKA_HUMAN	Q5VT25	77	343	267	14	NaN	NaN	NaN	AGC	AGC	KIAA0451	1.399119	5
5	AGC	CDC42BPB	HGNC:1738	MRCKB_HUMAN	Q9Y5S2	76	342	267	14	5OTFA	DFGin	BLAminus	AGC	AGC	KIAA1124	1.406213	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
493	TYR	TYK2_1	HGNC:12440	TYK2_HUMAN	P29597	589	868	280	26	3ZONA	DFGin	NaN	TYR	TYR	NaN	2.924877	7
494	TYR	TYK2_2	HGNC:12440	TYK2_HUMAN	P29597	897	1171	275	21	4GVJA	DFGin	BLAminus	TYR	TYR	NaN	2.031230	6
495	TYR	TYRO3	HGNC:12446	TYRO3_HUMAN	Q06418	518	788	271	10	NaN	NaN	NaN	TYR	TYR	BYK,DTK,RSE,SKY,TIF	2.428364	6
496	TYR	YES1	HGNC:12841	YES_HUMAN	P07947	277	528	252	10	NaN	NaN	NaN	TYR	TYR	YES	2.450070	0
497	TYR	ZAP70	HGNC:12858	ZAP70_HUMAN	P43403	337	595	259	10	1U59A	DFGin	BLAminus	TYR	TYR	SRK	2.194576	2

497 rows × 17 columns

A user can also create a custom color palette:

# construction of palette 
kinase_palette = {-1 : "#c7c7c7", "OTHER": "#c7c7c7", "None" :"#c7c7c7", "NA" : "#c7c7c7", "Uncharacterized" : "#c7c7c7", "root": "#000000",
                 "TYR": "#bd065f", "CMGC": "#d5c203", "TKL": "#997e73","STE": "#80b412", # kinase groups 
                 "CK1": "#0dbae9", "AGC": "#00bba1", "CAMK":  "#1f6ed4", "NEK": "#8ce4fa", "RGC":"#f59a62"}

df = read_embeddings(path_embedding, path_annotation, withroot=False)

result:                    pm1       pm2 1_Group    2_Gene      3_HGNC  4_Uni_entry  \
proteins_id                                                                  
1           -0.707390 -0.592394     AGC      AKT1    HGNC:391   AKT1_HUMAN   
2           -0.716039 -0.569608     AGC      AKT2    HGNC:392   AKT2_HUMAN   
3           -0.694588 -0.567908     AGC      AKT3    HGNC:393   AKT3_HUMAN   
4           -0.397254 -0.890340     AGC  CDC42BPA   HGNC:1737  MRCKA_HUMAN   
5           -0.393140 -0.887047     AGC  CDC42BPB   HGNC:1738  MRCKB_HUMAN   
...               ...       ...     ...       ...         ...          ...   
493          0.499884  0.780072     TYR    TYK2_1  HGNC:12440   TYK2_HUMAN   
494          0.218304  0.858630     TYR    TYK2_2  HGNC:12440   TYK2_HUMAN   
495          0.305759  0.830769     TYR     TYRO3  HGNC:12446  TYRO3_HUMAN   
496          0.402127  0.612690     TYR      YES1  HGNC:12841    YES_HUMAN   
497          0.469407  0.822037     TYR     ZAP70  HGNC:12858  ZAP70_HUMAN   

            5_Uni_acc  6_Domain_begin  7_Domain_end  8_Domain_length  \
proteins_id                                                            
1              P31749             150           408              259   
2              P31751             152           409              258   
3              Q9Y243             148           405              258   
4              Q5VT25              77           343              267   
5              Q9Y5S2              76           342              267   
...               ...             ...           ...              ...   
493            P29597             589           868              280   
494            P29597             897          1171              275   
495            Q06418             518           788              271   
496            P07947             277           528              252   
497            P43403             337           595              259   

             9_Largest_insert_length 10_PDB_validation  \
proteins_id                                              
1                                  9             6NPZB   
2                                  9             3E8DB   
3                                  9               NaN   
4                                 14               NaN   
5                                 14             5OTFA   
...                              ...               ...   
493                               26             3ZONA   
494                               21             4GVJA   
495                               10               NaN   
496                               10               NaN   
497                               10             1U59A   

            11_Conformational_state 12_Dihedral_state 13_Group_in_Uni  \
proteins_id                                                             
1                             DFGin          BLAminus             AGC   
2                             DFGin          BLAminus             AGC   
3                               NaN               NaN             AGC   
4                               NaN               NaN             AGC   
5                             DFGin          BLAminus             AGC   
...                             ...               ...             ...   
493                           DFGin               NaN             TYR   
494                           DFGin          BLAminus             TYR   
495                             NaN               NaN             TYR   
496                             NaN               NaN             TYR   
497                           DFGin          BLAminus             TYR   

            14_Group_in_Manning          15_Synonymn  evo_distance  \
proteins_id                                                          
1                           AGC              PKB,RAC      1.208266   
2                           AGC                  NaN      1.208312   
3                           AGC                 PKBG      1.183463   
4                           AGC             KIAA0451      1.399119   
5                           AGC             KIAA1124      1.406213   
...                         ...                  ...           ...   
493                         TYR                  NaN      2.924877   
494                         TYR                  NaN      2.031230   
495                         TYR  BYK,DTK,RSE,SKY,TIF      2.428364   
496                         TYR                  YES      2.450070   
497                         TYR                  SRK      2.194576   

             decile_domain  
proteins_id                 
1                        2  
2                        2  
3                        2  
4                        5  
5                        5  
...                    ...  
493                      7  
494                      6  
495                      6  
496                      0  
497                      2  

[497 rows x 19 columns]

Here follow several examples of kinase family visualization.

Color by kinase group

trace1 = plot_embedding_interactive(df, 
                                    labels_name = '1_Group',#'1_Group',#'2_Gene', 
                                    show_text=True, 
                                    color_palette = kinase_palette,
                                    title = "Poinicaré Map projection colored by kinase groups", 
                                    fontsize = 10,
                              )
trace1.write_image(path_to_figures+"/Kinases_by_group.pdf")
trace1.show()

Index(['1_Group', '2_Gene', '3_HGNC', '4_Uni_entry', '5_Uni_acc',
       '6_Domain_begin', '7_Domain_end', '8_Domain_length',
       '9_Largest_insert_length', '10_PDB_validation',
       '11_Conformational_state', '12_Dihedral_state', '13_Group_in_Uni',
       '14_Group_in_Manning', '15_Synonymn', 'evo_distance', 'decile_domain'],
      dtype='object')

You can highlight several points by using plotly iterface. Provide the corresponding column name in second_labels_name and a list of labels to show in labels_text:

trace2 = plot_embedding_interactive(df, 
                                    labels_name = '1_Group',#'1_Group',#'2_Gene', 
                                    show_text=True, 
                                    color_palette = kinase_palette,
                                    title = "Poinicaré Map projection colored by kinase groups", 
                                    fontsize = 10,
                                    second_labels_name = "4_Uni_entry", 
                                    #labels_text = ["ST17B_HUMAN", "MYLK2_HUMAN", "KALRN_HUMAN"]
                                    labels_text = ["CLK3_HUMAN", "SRPK3_HUMAN", "HIPK1_HUMAN","CSK22_HUMAN"] # some CMGC kinase
                                    #second_labels_name = "2_Gene",
                                    #labels_text = ["RPS6KA1_1", "RPS6KA2_1", "RPS6KA5_1", "RPS6KB2", "RPS6KA1_2", "RPS6KA2_2",  "RPS6KA3_2",  "RPS6KA5_2"] # some sequences of AGC first domain and CAMK second domain (mentionned in the article) to label
                                    
                              )
trace2.show()

Index(['1_Group', '2_Gene', '3_HGNC', '4_Uni_entry', '5_Uni_acc',
       '6_Domain_begin', '7_Domain_end', '8_Domain_length',
       '9_Largest_insert_length', '10_PDB_validation',
       '11_Conformational_state', '12_Dihedral_state', '13_Group_in_Uni',
       '14_Group_in_Manning', '15_Synonymn', 'evo_distance', 'decile_domain'],
      dtype='object')

You can also compare different colorings by assigning the labels_name variable a name of the desired column. Here we compare the kinase group coloring proposed in the article to the Uniprot name of kinase:

trace3 = plot_embedding_interactive(df, 
                                    labels_name = '13_Group_in_Uni',#'1_Group',#'2_Gene', 
                                    show_text=True, 
                                    color_palette = kinase_palette,
                                    title = "PM projection on kinases by kinase groups in Uniprot - KNN 5 gamma 3 batchsize 4 epochs 1000",
                                    fontsize = 10,
                                    )
trace3.write_image(path_to_figures+"/Kinases_by_UniGroup.pdf")
trace3.show()

Index(['1_Group', '2_Gene', '3_HGNC', '4_Uni_entry', '5_Uni_acc',
       '6_Domain_begin', '7_Domain_end', '8_Domain_length',
       '9_Largest_insert_length', '10_PDB_validation',
       '11_Conformational_state', '12_Dihedral_state', '13_Group_in_Uni',
       '14_Group_in_Manning', '15_Synonymn', 'evo_distance', 'decile_domain'],
      dtype='object')

...and to the classification provided in Manning et al. study:

trace4 = plot_embedding_interactive(df, 
                                    labels_name = '14_Group_in_Manning',#'1_Group',#'2_Gene', 
                                    show_text=True, 
                                    color_palette = kinase_palette,
                                    second_labels_name = "2_Gene", 
                                    title = "Poincaré Maps projection by kinases groups according to Manning",
                                    fontsize = 10, 
                                    #labels_text = ["MAP3K7, MAP3K9", "MAP3K10", "MAP3K11", "MAP3K12", "MAP3K13", "MAP3K20", "MAP3K21"] # STE dans TKL
                                    #labels_text = ["AURKA", "AURKB", "AURKC", "CAMKK1", "CAMKK2", "PLK1", "PLK2", "PLK3", "PLK4"] # 10 kinases pourraient être CAMK
                                    
                              )
trace4.write_image(path_to_figures+"/Kinases_by_ManningGroup.pdf")
trace4.show()

Index(['1_Group', '2_Gene', '3_HGNC', '4_Uni_entry', '5_Uni_acc',
       '6_Domain_begin', '7_Domain_end', '8_Domain_length',
       '9_Largest_insert_length', '10_PDB_validation',
       '11_Conformational_state', '12_Dihedral_state', '13_Group_in_Uni',
       '14_Group_in_Manning', '15_Synonymn', 'evo_distance', 'decile_domain'],
      dtype='object')